Anzeige
Erschienen in:
2014 | OriginalPaper | Buchkapitel
SVM Tree for Personalized Transductive Learning in Bioinformatics Classification Problems
verfasst von : Maurizio Fiasché
Erschienen in: Recent Advances of Neural Network Models and Applications
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Personalized modelling joint with Transductive Learning (PTL) uses a particular local modelling (personalized) around a single point for classification of each test sample, thus it is basically neighbourhood dependent. Usually existing PTL methods define the neighbourhood using a (dis)similarity measure, in this paper we propose a new transductive SVM classification tree (tSVMT) based on PTL. The neighbourhood of a test sample is built over the classification knowledge modelled by regional SVMs, and a set of such SVMs adjacent to the test sample are aggregated systematically into a tSVMT. Compared to a normal SVM/SVMT approach, the proposed tSVMT, with the aggregation of SVMs, improves classifying power in terms of accuracy on bioinformatics database. Moreover, tSVMT seems to solve the over-fitting problem of all previous SVMTs as it aggregates neighbourhood knowledge, significantly reducing the size of the SVM tree.